Relationship of behavioral risk factors for chronic diseases and preventive health services utilization among adults, aged 50+, from eleven European countries

Linardakis, Manolis; Papadaki, Angeliki; Smpokos, Emmanouil; Micheli, Katerina; Vozikaki, Maria; Philalithis, Αnastas

doi:10.1007/s10389-015-0683-6

Cited by 10 publications

(9 citation statements)

References 23 publications

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“…Therefore, the study raises societal questions as to why social isolation might be related with health status, and so, points to a way to reduce the increase in healthcare expenditures. Intuitively, it is to be expected that if a person with greater social participation has a lower risk of suffering from multiple chronic diseases, this would be associated with low health care utilization [ 23 – 24 ]. As previously claimed by Becchetti et al [ 25 ], the identification of the different factors, as well as their heterogeneous impact across different population groups is decisive when tackling the challenge of improving health outcomes without threatening the sustainability of health care systems.…”

Section: Discussionmentioning

confidence: 99%

Social isolation and multiple chronic diseases after age 50: A European macro-regional analysis

2018

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BackgroundDifferent studies have found that socioeconomic determinants influence the prevalence of chronic diseases in older people. However, there has been relatively little research on the incidence of how social isolation may affect them. We suggest that social isolation is a serious concern for people living with chronic illnesses.MethodIn this paper, we examine whether there is an increase in the propensity of being diagnosed with chronic illnesses because of a decrease in social relations for elderly Europeans. We have used a panel data for Waves 1–6 (2004–2015) of Survey on Health, Ageing and Retirement in Europe (SHARE) and logistic regressions. Besides, we have studied three geographic macro-areas (Nordic, Continental and Southern). Being diagnosed with three or more chronic diseases is considered as a dependent variable, and as social control variables we have used three isolation proxies (living alone, providing help to family, friends or neighbours and participation-club activities). Other socio-demographic variables are included (gender, age, educational level, job situation, area of location and quality of life).ResultsOur results for the full sample indicate that people who participate in social activities have fewer probability of suffering from chronic diseases (OR = 0.70, 95% CI 0.54, 0.92). For people who live alone the reverse effect is observed (OR = 1.20, 95% CI 1.04, 1.39). Differences are shown by macro-areas, e.g. providing help (OR = 0.58, 95% CI 0.34, 0.97) isolation proxy is significant for the Nordic macro-area. Club-participation activities and living alone are significant for Continental and Southern macro-areas, respectively (OR = 0.65, 95% CI 0.55, 0.82; OR = 1.46, 95% CI 1.21, 1.77).ConclusionsSocial isolation increases the risk of being diagnosed with chronic illnesses. That is, people with greater social participation have lower risk of suffering from multiple chronic diseases. This risk linked to isolation, together with the traditional one associated with lifestyles, should be considered in the development of new public policies.

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Section: Discussionmentioning

confidence: 99%

Social isolation and multiple chronic diseases after age 50: A European macro-regional analysis

2018

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“…Through secondary analysis of all available primary care setting data [ 22 ], four MBRFs were assessed, including smoking, increased BMI [ 3 , 23 ], sleep deprivation, and stress level. Smokers were classified as those who smoke more than one cigarette per day [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

Multiple Behavioral Risk Factors As Assets for Chronic Disease Prevention: Observations From Urban Primary Care Settings in Crete, Greece

Symvoulakis,

Stachteas,

Smyrnakis

et al. 2024

Cureus

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Aim: This study aimed to assess the health profile of patient-attendees visiting primary healthcare (PHC) practice settings in the midst of the COVID-19 pandemic and to explore the relationships between multiple behavioral risk factors (MBRFs) and consultation-driven health information. Multiple behavioral risk factors involve a variety of unhealthy behaviors that are associated with an increased prevalence of non-communicable diseases (NCDs). Subjects and methods: The study design was based on a dataset analysis, afterward exploring the feasibility and diagnostic capacity of respiratory morbidity aspects from a study previously conducted. The study dataset contained information regarding socio-demographic characteristics, health habits, clinical information, and reported comorbidities from 183 primary care patient-attendees. A categorical regression analysis was performed, using as a numeric variable the multiple MBRFs (clustering of 0 to four factors) in order to examine relationships with the basic and clinical characteristics of the patient-attendees. Results: Based on this secondary analysis, it was found that the prevalence of MBRFs is quite common among patient-attendees visiting urban PHC facilities. The prevalence of current smoking, sleep deprivation, increased body weight, and medium/high perceived stress levels were 33.9%, 52.5%, 83.1%, and 35.0%, respectively. An increased occurrence of MBRFs might be significantly predicted by the lower age of patient-attendees (b = -0.221, p = 0.05), by the absence of gray hair at an early age (b = -0.144, p = 0.042), by the physical discomfort during activities (b = 0.191, p = 0.017), or by the lower oxygen saturation (b = -0.184, p = 0.004). Diabetes mellitus (25.1%) was the most prevalent condition, followed by bronchial asthma (18.6%) and depression (15.8%). Conclusions: Lower age, absence of premature hair whitening, physical discomfort during activities, and lower oxygen saturation are linked with an increased occurrence of MBRFs, leading to a neglected way of living. Those factors could be used to alert researchers, policymakers, and PHC professionals to act accordingly in order to prevent or early diagnose NCDs.

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“…Binomial and chi-square (χ 2 ) tests and 95% confidence intervals (95% CIs) were used to compare responses. A hierarchical cluster analysis was performed to identify relative and homogeneous factor groups (12)(13) . This method allowed the classification of the characteristics in groups as well as between clinics based upon similar features, such as common behaviors during job procedures.…”

Section: Methodsmentioning

confidence: 99%

Features of recording practices and communication during nursing handover: a cluster analysis

Rikos

Linardakis

Rovithis

et al. 2018

Rev. esc. enferm. USP

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Objective: To record and identify the characteristics of nursing handovers in a tertiary hospital. Method: Observational study. Twenty-two nurses participated in 11 nursing handovers in 2015/16, using a recorded audio system and an unstructured observation form. Hierarchical cluster analysis was performed. Results: Thirty characteristics were identified. The nursing handovers were based on the clinical status of patients, and all nurses obtained specialized scientific knowledge specific to the clinical environment. The information used was not based on nursing diagnoses and not in accordance with best nursing clinical practice. The following four clusters emerged among the 30 characteristics: 1) the use of evidence-based nursing practice, 2) the nonuse of evidence-based nursing practice and its correlation with strained psychological environment, 3) patient management and the clinical skills/knowledge of nurses, and 4) handover content, quality of information transferred and specialization. Conclusion: Multiple characteristics were observed. The majority of characteristics were grouped based on common features, and 4 main clusters emerged. The investigation and understanding of structural relations between these characteristics and their respective clusters may lead to an improvement in the quality of nursing health care services.

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Relationship of behavioral risk factors for chronic diseases and preventive health services utilization among adults, aged 50+, from eleven European countries

Cited by 10 publications

References 23 publications

Social isolation and multiple chronic diseases after age 50: A European macro-regional analysis

Social isolation and multiple chronic diseases after age 50: A European macro-regional analysis

Multiple Behavioral Risk Factors As Assets for Chronic Disease Prevention: Observations From Urban Primary Care Settings in Crete, Greece

Features of recording practices and communication during nursing handover: a cluster analysis

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